Past makes future: role of pFC in prediction

Bridging flexible goal-directed cognition and consciousness: The Goal-Aligning Representation Internal Manipulation theory

Goal-directed manipulation of internal representations is a key element of human flexible behaviour, while consciousness is commonly associated with higher-order cognition and human flexibility. Current perspectives have only partially linked these processes, thus preventing a clear understanding of how they jointly generate flexible cognition and behaviour. Moreover, these limitations prevent an effective exploitation of this knowledge for technological scopes. We propose a new theoretical perspective that extends our 'three-component theory of flexible cognition' toward higher-order cognition and consciousness, based on the systematic integration of key concepts from Cognitive Neuroscience and AI/Robotics. The theory proposes that the function of conscious processes is to support the alignment of representations with multi-level goals. This higher alignment leads to more flexible and effective behaviours. We analyse here our previous model of goal-directed flexible cognition (validated with more than 20 human populations) as a starting GARIM-inspired model. By bridging the main theories of consciousness and goal-directed behaviour, the theory has relevant implications for scientific and technological fields. In particular, it contributes to developing new experimental tasks and interpreting clinical evidence. Finally, it indicates directions for improving machine learning and robotics systems and for informing real-world applications (e.g., in digital-twin healthcare and roboethics).

The significance of structural rich club hubs for the processing of hierarchical stimuli

The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.

Anti-LINGO-1 antibody protects neurons and synapses in the medial prefrontal cortex of APP/PS1 transgenic mice

The medial prefrontal cortex (mPFC), one of the most vulnerable brain regions in Alzheimer's disease (AD), plays a critical role in cognition. Leucine-rich repeat and immunoglobulin-like domain-containing nogo receptor-interacting protein-1 (LINGO-1) negatively affects nerve growth in the central nervous system; however, its role in the pathological damage to the mPFC remains to be studied in AD. In this study, an anti-LINGO-1 antibody was administered to 10-month-old APP/PS1 mice, and behavioral tests, stereological methods, immunohistochemistry and immunofluorescence were used to answer this question. Our results revealed that LINGO-1 was highly expressed in the neurons of the mPFC of AD mice, and the anti-LINGO-1 antibody improved prefrontal cortex-related function and reduced the protein level of LINGO-1, atrophy of the volume, Aβ deposition and massive losses of synapses and neurons in the mPFC of AD mice. Antagonizing LINGO-1 could effectively alleviate the pathological damage in the mPFC of AD mice, which might be an important structural basis for improving prefrontal cortex-related function. Abnormal expression of LINGO-1 in the mPFC may be one of the key targets of AD, and the effect initiated by the anti-LINGO-1 antibody may provide an important basis in the search for drugs for the prevention and treatment of AD.

A Perspective on Lifelong Open-Ended Learning Autonomy for Robotics through Cognitive Architectures

This paper addresses the problem of achieving lifelong open-ended learning autonomy in robotics, and how different cognitive architectures provide functionalities that support it. To this end, we analyze a set of well-known cognitive architectures in the literature considering the different components they address and how they implement them. Among the main functionalities that are taken as relevant for lifelong open-ended learning autonomy are the fact that architectures must contemplate learning, and the availability of contextual memory systems, motivations or attention. Additionally, we try to establish which of them were actually applied to real robot scenarios. It transpires that in their current form, none of them are completely ready to address this challenge, but some of them do provide some indications on the paths to follow in some of the aspects they contemplate. It can be gleaned that for lifelong open-ended learning autonomy, motivational systems that allow finding domain-dependent goals from general internal drives, contextual long-term memory systems that all allow for associative learning and retrieval of knowledge, and robust learning systems would be the main components required. Nevertheless, other components, such as attention mechanisms or representation management systems, would greatly facilitate operation in complex domains.

The Active Inference Model of Coherence Therapy

Coherence Therapy is an empirically derived experiential psychotherapy based on Psychological Constructivism. Symptoms are viewed as necessary output from an implicit model of the world. The therapist curates experiences and directs attention toward discovering the model. Rendered explicit, the model is juxtaposed with contradictory knowledge driving memory re-consolidation with resolution of the symptom. The Bayesian Brain views perception and action as inferential processes. Prior beliefs are combined in a generative model to explain the hidden causes of sensations through a process of Active Inference. Prior beliefs that are poor fits to the real world are suboptimal. Suboptimal priors with optimal inference produce Bayes Optimal Pathology with behavioral symptoms. The Active Inference Model of Coherence Therapy posits that Coherence Therapy is a dyadic act of therapist guided Active Inference that renders the (probable) hidden causes of a client's behavior conscious. The therapist's sustained attention on the goal of inference helps to overcome memory control bias against retrieval of the affectively charged suboptimal prior. Serial experiences cue memory retrieval and re-instantiation of the physiological/affective state that necessitates production of the symptom in a particular context. As this process continues there is a break in modularity with assimilation into broader networks of experience. Typically, the symptom produced by optimal inference with the suboptimal prior is experienced as unnecessary/inappropriate when taken out of the particular context. The implicit construct has been re-represented and rendered consciously accessible, by a more complex but more accurate model in which the symptom is necessary in some contexts but not others. There is an experience of agency and control in symptom creation, accompanied by the spontaneous production of context appropriate behavior. The capacity for inference has been restored. The Active Inference Model of Coherence Therapy provides a framework for Coherence Therapy as a computational process which can serve as the basis for new therapeutic interventions and experimental designs integrating biological, cognitive, behavioral, and environmental factors.

Memory-Based Prediction Deficits and Dorsolateral Prefrontal Dysfunction in Schizophrenia

BACKGROUND

Theories suggest that people with schizophrenia (SZ) have problems generating predictions based on past experiences. The dorsolateral prefrontal cortex (DLPFC) and hippocampus participate in memory-based prediction. We used functional magnetic resonance imaging to investigate DLPFC and hippocampal function in healthy control (HC) subjects and people with SZ during memory-based prediction.

METHODS

Prior to scanning, HC subjects (n = 54) and people with SZ (n = 31) learned 5-object sequences presented in fixed or random orders on each repetition. During scanning, participants made semantic decisions (e.g., "Can this object fit in a shoebox?") on a continuous stream of objects from fixed and random sequences. Sequence prediction was demonstrated by faster semantic decisions for objects in fixed versus random sequences because memory could be used to anticipate and more efficiently process semantic information about upcoming objects in fixed sequences. Representational similarity analyses were used to determine how each sequence type was represented in the posterior hippocampus and DLPFC.

RESULTS

Sequence predictions were reduced in individuals with SZ relative to HC subjects. Representational similarity analyses revealed stronger memory-based predictions in the DLPFC of HC subjects than people with SZ, and DLPFC representations correlated with more successful predictions in HC subjects only. For the posterior hippocampus, voxel pattern similarity was increased for fixed versus random sequences in HC subjects only, but no significant between-group differences or correlations with prediction success were observed.

CONCLUSIONS

Individuals with SZ are capable of learning temporal sequences; however, they are impaired using memory to predict upcoming events as efficiently as HC subjects. This deficit appears related to disrupted neural representation of sequence information in the DLPFC.

Cortex-wide response mode of VIP-expressing inhibitory neurons by reward and punishment

Neocortex is classically divided into distinct areas, each specializing in different function, but all could benefit from reinforcement feedback to inform and update local processing. Yet it remains elusive how global signals like reward and punishment are represented in local cortical computations. Previously, we identified a cortical neuron type, vasoactive intestinal polypeptide (VIP)-expressing interneurons, in auditory cortex that is recruited by behavioral reinforcers and mediates disinhibitory control by inhibiting other inhibitory neurons. As the same disinhibitory cortical circuit is present virtually throughout cortex, we wondered whether VIP neurons are likewise recruited by reinforcers throughout cortex. We monitored VIP neural activity in dozens of cortical regions using three-dimensional random access two-photon microscopy and fiber photometry while mice learned an auditory discrimination task. We found that reward and punishment during initial learning produce rapid, cortex-wide activation of most VIP interneurons. This global recruitment mode showed variations in temporal dynamics in individual neurons and across areas. Neither the weak sensory tuning of VIP interneurons in visual cortex nor their arousal state modulation was fully predictive of reinforcer responses. We suggest that the global response mode of cortical VIP interneurons supports a cell-type-specific circuit mechanism by which organism-level information about reinforcers regulates local circuit processing and plasticity.

AI and We in the Future in the Light of the Ouroboros Model: A Plea for Plurality

Artificial Intelligence (AI) is set to play an ever more important role in our lives and societies. Here, some boundary conditions and possibilities for shaping and using AI as well as advantageously embedding it in daily life are sketched. On the basis of a recently proposed cognitive architecture that claims to deliver a general layout for both natural intelligence and general AI, a coarse but broad perspective is developed and an emphasis is put on AI ethics. A number of findings, requirements, and recommendations are derived that can transparently be traced to the hypothesized structure and the procedural operation of efficient cognitive agents according to the Ouroboros Model. Including all of the available and possibly relevant information for any action and respecting a “negative imperative” are the most important resulting recommendations. Self-consistency, continual monitoring, equitable considerations, accountability, flexibility, and pragmatic adaptations are highlighted as foundations and, at the same time, mandatory consequences for timely answers to the most relevant questions concerning the embedding of AI in society and ethical rules for this.

Neurobiological Links between Stress, Brain Injury, and Disease

Stress, which refers to a combination of physiological, neuroendocrine, behavioral, and emotional responses to novel or threatening stimuli, is essentially a defensive adaptation under physiological conditions. However, strong and long-lasting stress can lead to psychological and pathological damage. Growing evidence suggests that patients suffering from mild and moderate brain injuries and diseases often show severe neurological dysfunction and experience severe and persistent stressful events or environmental stimuli, whether in the acute, subacute, or recovery stage. Previous studies have shown that stress has a remarkable influence on key brain regions and brain diseases. The mechanisms through which stress affects the brain are diverse, including activation of endoplasmic reticulum stress (ERS), apoptosis, oxidative stress, and excitatory/inhibitory neuron imbalance, and may lead to behavioral and cognitive deficits. The impact of stress on brain diseases is complex and involves impediment of recovery, aggravation of cognitive impairment, and neurodegeneration. This review summarizes various stress models and their applications and then discusses the effects and mechanisms of stress on key brain regions—including the hippocampus, hypothalamus, amygdala, and prefrontal cortex—and in brain injuries and diseases—including Alzheimer’s disease, stroke, traumatic brain injury, and epilepsy. Lastly, this review highlights psychological interventions and potential therapeutic targets for patients with brain injuries and diseases who experience severe and persistent stressful events.

Cognitive Networks (Cognits) Process and Maintain Working Memory

Ever since it was discovered in the monkey’s prefrontal cortex, persistent neuronal activity during the delay period of delay tasks has been considered a phenomenon of working memory. Operationally, this interpretation is correct, because during that delay those tasks require the memorization of a sensory cue, commonly visual. What is incorrect is the assumption that the persistent activity during the delay is caused exclusively by the retention of the sensory cue. In this brief review, the author takes the position that the neural substrate of working memory is an array of long-term memory networks, that is, of cognitive networks (cognits), updated and orderly activated for the attainment of a behavioral goal. In the case of a behavioral task, that activated array of cognits has been previously formed in long-term memory (throughout this text, the expression “long-term memory” refers to all experiences acquired after birth, including habits and so-called procedural memory, such as the learning of a behavioral task). The learning of a task is the forming of synaptic associations between neural representations of three cognitive components of the task: perceptual, motor, and reward-related. Thereafter, when needed, the composite cognit of the task is activated in an orderly fashion to serve working memory in the perception-action cycle. To make his points on a complex issue, which has been the focus of his work, and to delineate a frontier for future research, the author refers to several of his own publications and previously published reviews.

Altered Structural Covariance of Insula, Cerebellum and Prefrontal Cortex Is Associated with Somatic Symptom Levels in Irritable Bowel Syndrome (IBS)

Somatization, defined as the presence of multiple somatic symptoms, frequently occurs in irritable bowel syndrome (IBS) and may constitute the clinical manifestation of a neurobiological sensitization process. Brain imaging data was acquired with T1 weighted 3 tesla MRI, and gray matter morphometry were analyzed using FreeSurfer. We investigated differences in networks of structural covariance, based on graph analysis, between regional gray matter volumes in IBS-related brain regions between IBS patients with high and low somatization levels, and compared them to healthy controls (HCs). When comparing IBS low somatization (N = 31), IBS high somatization (N = 35), and HCs (N = 31), we found: (1) higher centrality and neighbourhood connectivity of prefrontal cortex subregions in IBS high somatization compared to healthy controls; (2) higher centrality of left cerebellum in IBS low somatization compared to both IBS high somatization and healthy controls; (3) higher centrality of the anterior insula in healthy controls compared to both IBS groups, and in IBS low compared to IBS high somatization. The altered structural covariance of prefrontal cortex and anterior insula in IBS high somatization implicates that prefrontal processes may be more important than insular in the neurobiological sensitization process associated with IBS high somatization.

Internal manipulation of perceptual representations in human flexible cognition: A computational model

Executive functions represent a set of processes in goal-directed cognition that depend on integrated cortical-basal ganglia brain systems and form the basis of flexible human behaviour. Several computational models have been proposed for studying cognitive flexibility as a key executive function and the Wisconsin card sorting test (WCST) that represents an important neuropsychological tool to investigate it. These models clarify important aspects that underlie cognitive flexibility, particularly decision-making, motor response, and feedback-dependent learning processes. However, several studies suggest that the categorisation processes involved in the solution of the WCST include an additional computational stage of category representation that supports the other processes. Surprisingly, all models of the WCST ignore this fundamental stage and they assume that decision making directly triggers actions. Thus, we propose a novel hypothesis where the key mechanisms of cognitive flexibility and goal-directed behaviour rely on the acquisition of suitable representations of percepts and their top-down internal manipulation. Moreover, we propose a neuro-inspired computational model to operationalise this hypothesis. The capacity of the model to support cognitive flexibility was validated by systematically reproducing and interpreting the behaviour exhibited in the WCST by young and old healthy adults, and by frontal and Parkinson patients. The results corroborate and further articulate the hypothesis that the internal manipulation of representations is a core process in goal-directed flexible cognition.

Principles of Integrated Cognitive Training for Executive Attention: Application to an Instrumental Skill

One effective cognitive treatment is the rehabilitation of working memory (WM) using an integrated approach that targets the “executive attention” system. Recent neuroscientific literature has revealed that treatment efficacy depends on the presence of various features, such as adaptivity, empathy, customization, avoidance of automatism and stereotypies, and alertness activation. Over the last two decades, an Integrated Cognitive Training (ICT) protocol has been proposed and developed; ICT takes the above-mentioned features and existing literature into account, and has been used to promote the development of reading skills. ICT has been employed in several clinical settings and involves stimulation of a specific deteriorated system (e.g., reading) and the improvement of executive attention components, thus also increasing working memory capacity. In this context, we present two experiments. In Experiment 1, participants diagnosed with dyslexia (aged between 8 and 14 years) underwent two ICT sessions a week, with home supplements, for a duration of 7 months. The participants showed a significant improvement in the reading speed of text, words, and non-words, and in the reading accuracy of text and non-words. In Experiment 2, we replicated Experiment 1, but included a comparison between two groups (experimental group vs. control group) of young participants with diagnosis of dyslexia. The experimental group was subjected to 18 ICT sessions twice a week and with home supplements, using the same protocol as in Experiment 1. The control group was entrusted to the protocol of compensatory tools and dispense/helping procedures provided by the scholastic Personalized Educational Plan. After training, the experimental group gained about 0.5 syllables per second in text reading, and a marked decrease in error rate. The control group showed no significant improvement in reading skills after the same period. Moreover, the improvement observed in the experimental group remained stable 4 months after ICT had ended. The results of these two experiments support the efficacy of the integrated ICT protocol in improving reading skills in children with dyslexia and its sustained effect.

Volumetric MRI Analysis of Brain Structures in Patients with History of First and Repeated Suicide Attempts: A Cross Sectional Study

Structural brain changes are found in suicide attempters and in patients with mental disorders. It remains unclear whether the suicidal behaviors are related to atrophy of brain regions and how the morphology of specific brain areas is changing with each suicide attempt. The sample consisted of 56 patients hospitalized after first suicide attempt (first SA) (n = 29), more than one suicide attempt (SA > 1) (n = 27) and 54 healthy controls (HC). Brain volume was measured using FreeSurfer 6.0 automatic segmentation technique. In comparison to HC, patients with first SA had significantly lower cortical thickness of the superior and rostral middle frontal areas, the inferior, middle and superior temporal areas of the left hemisphere and superior frontal area of the right hemisphere. In comparison to HC, patients after SA > 1 had a significantly lower cortical thickness in ten areas of frontal cortex of the left hemisphere and seven areas of the right hemisphere. The comparison of hippocampus volume showed a significantly lower mean volume of left and right parts in patients with SA > 1, but not in patients with first SA. The atrophy of frontal, temporal cortex and hippocampus parts was significantly higher in repeated suicide attempters than in patients with first suicide attempt.

Cingulate-basal ganglia-thalamo-cortical aspects of catatonia and implications for treatment

The catatonic syndrome is an example of a multifactorial neurobehavioral disorder that causes much morbidity and mortality but also has the potential to unlock the mystery of how motivation and movement interact to produce behavior. In this chapter, an attempt is made to understand better the catatonic syndrome through the lens of neurobiology and neuropathophysiology updated by recent studies in molecular biology, genomics, inflammasomics, neuroimaging, neural network theory, and neuropsychopathology. This will result in a neurostructural model for the catatonic syndrome that centers on paralimbic regions including the anterior and midcingulate cortices, as they interface with striatal and thalamic nodes in the salience decision-making network. Examination of neurologic disorders like the abulic syndrome, which includes in its extreme catatonic form, akinetic mutism, will identify the cingulate cortex and paralimbic neighbors as regions of interest. This exploration has the potential to unlock mysteries of the brain cascade from motivation to movement and to clarify catatonia therapeutics. Such a synthesis may also help us discern meaning inherent in this complex neurobehavioral syndrome.

Interindividual differences in gray and white matter properties are associated with early complex motor skill acquisition

Brain circuits mediate but also constrain experience-induced plasticity and corresponding behavioral changes. Here we tested whether interindividual behavioral differences in learning a challenging new motor skill correlate with variations in brain anatomy. Young, healthy participants were scanned using structural magnetic resonance imaging (T1-weighted MPRAGE, n = 75 and/or diffusion-weighted MRI, n = 59) and practiced a complex whole-body balancing task on a seesaw-like platform. Using conjunction tests based on the nonparametric combination (NPC) methodology, we found that gray matter volume (GMV) in the right orbitrofrontal cortex was positively related to the subjects' initial level of proficiency and their ability to improve performance during practice. Similarly, we obtained a strong trend toward a positive correlation between baseline fractional anisotropy (FA) in commissural prefrontal fiber pathways and later motor learning. FA results were influenced more strongly by radial than axial diffusivity. However, we did not find unique anatomical correlates of initial performance and learning to rate. Our findings reveal structural predispositions for successful motor skill performance and acquisition in frontal brain structures and underlying frontal white matter tracts. Together with previous results, these findings support the view that structural constraints imposed by the brain determine subsequent behavioral success and underline the importance of structural brain network constitution before learning starts.

The Predictive Processing Model of EMDR

Eye Movement Desensitization and Reprocessing Therapy (EMDR) is an effective treatment for Post-traumatic Stress Disorder (PTSD). The Adaptive Information Processing Model (AIP) guides the development and practice of EMDR. The AIP postulates inadequately processed memory as the foundation of PTSD pathology. Predictive Processing postulates that the primary function of the brain is prediction that serves to anticipate the next moment of experience in order to resist the dissipative force of entropy thus facilitating continued survival. Memory is the primary substrate of prediction, and is optimized by an ongoing process of precision weighted prediction error minimization that refines prediction by updating the memories on which it is based. The Predictive Processing model of EMDR postulates that EMDR facilitates the predictive processing of traumatic memory by overcoming the bias against exploration and evidence accumulation. The EMDR protocol brings the traumatic memory into an active state of re-experiencing. Defensive responding and/or low sensory precision preclude evidence accumulation to test the predictions of the traumatic memory in the present. Sets of therapist guided eye movements repeatedly challenge the bias against evidence accumulation and compel sensory sampling of the benign present. Eye movements reset the theta rhythm organizing the flow of information through the brain, facilitating the deployment of both overt and covert attention, and the mnemonic search for associations. Sampling of sensation does not support the predictions of the traumatic memory resulting in prediction error that the brain then attempts to minimize. The net result is a restoration of the integrity of the rhythmic deployment of attention, a recalibration of sensory precision, and the updating (reconsolidation) of the traumatic memory. Thus one prediction of the model is a decrease in Attention Bias Variability, a core dysfunction in PTSD, following successful treatment with EMDR.

Encoding of the Intent to Drink Alcohol by the Prefrontal Cortex Is Blunted in Rats with a Family History of Excessive Drinking

The prefrontal cortex (PFC) plays a central role in guiding decision making, and its function is altered by alcohol use and an individual's innate risk for excessive alcohol drinking. The primary goal of this work was to determine how neural activity in the PFC guides the decision to drink. Towards this goal, the within-session changes in neural activity were measured from medial PFC (mPFC) of rats performing a drinking procedure that allowed them to consume or abstain from alcohol in a self-paced manner. Recordings were obtained from rats that either lacked or expressed an innate risk for excessive alcohol intake, Wistar or alcohol-preferring (P) rats, respectively. Wistar rats exhibited patterns of neural activity consistent with the intention to drink or abstain from drinking, whereas these patterns were blunted or absent in P rats. Collectively, these data indicate that neural activity patterns in mPFC associated with the intention to drink alcohol are influenced by innate risk for excessive alcohol drinking. This observation may indicate a lack of control over the decision to drink by this otherwise well-validated supervisory brain region.

Prefrontal excitatory/inhibitory balance in stress and emotional disorders: Evidence for over-inhibition

Chronic stress-induced emotional disorders like anxiety and depression involve imbalances between the excitatory glutamatergic system and the inhibitory GABAergic system in the prefrontal cortex (PFC). However, the precise nature and trajectory of excitatory/inhibitory (E/I) imbalances in these conditions is not clear, with the literature reporting glutamatergic and GABAergic findings that are at times contradictory and inconclusive. Here we propose and discuss the hypothesis that chronic stress-induced emotional dysfunction involves hypoactivity of the PFC due to increased inhibition. We will also discuss E/I imbalances in the context of sex differences. In this review, we will synthesize research about how glutamatergic and GABAergic systems are perturbed by chronic stress and in related emotional disorders like anxiety and depression and propose ideas for reconciling contradictory findings in support of the hypothesis of over-inhibition. We will also discuss evidence for how aspects of the GABAergic system such as parvalbumin (PV) cells can be targeted therapeutically for reinstating activity and plasticity in the PFC and treating stress-related disorders.

Discovering and characterizing dynamic functional brain networks in task FMRI

Many existing studies for the mapping of function brain networks impose an implicit assumption that the networks' spatial distributions are constant over time. However, the latest research reports reveal that functional brain networks are dynamical and have time-varying spatial patterns. Furthermore, how these functional networks evolve over time has not been elaborated and explained in sufficient details yet. In this paper, we aim to discover and characterize the dynamics of functional brain networks via a windowed group-wise dictionary learning and sparse coding approach. First, we aggregated the sampled subjects' fMRI signals into one big data matrix, and learned a common dictionary for all individuals via a group-wise dictionary learning step. Second, we obtained the dynamic time-varying functional networks by using the windowed time-varying sparse coding approach. Experimental results demonstrated that our windowed group-wise dictionary learning and sparse coding method can effectively detect the task-evoked networks and also characterize how these networks evolve over time. This work sheds novel insights on the dynamics mechanism of functional brain networks.

Perceptual Generalization and Context in a Network Memory Inspired Long-Term Memory for Artificial Cognition

In the framework of open-ended learning cognitive architectures for robots, this paper deals with the design of a Long-Term Memory (LTM) structure that can accommodate the progressive acquisition of experience-based decision capabilities, or what different authors call "automation" of what is learnt, as a complementary system to more common prospective functions. The LTM proposed here provides for a relational storage of knowledge nuggets given the form of artificial neural networks (ANNs) that is representative of the contexts in which they are relevant in a configural associative structure. It also addresses the problem of continuous perceptual spaces and the task- and context-related generalization or categorization of perceptions in an autonomous manner within the embodied sensorimotor apparatus of the robot. These issues are analyzed and a solution is proposed through the introduction of two new types of knowledge nuggets: P-nodes representing perceptual classes and C-nodes representing contexts. The approach is studied and its performance evaluated through its implementation and application to a real robotic experiment.

Decreased Task-Related HRV Is Associated With Inhibitory Dysfunction Through Functional Inter-Region Connectivity of PFC in Major Depressive Disorder

The regulation of the autonomic nervous system (ANS) can improve cognitive function in major depressive disorders (MDD). Heart rate variability (HRV) derives from the dynamic control of the ANS and reflects the balance between the activities of the sympathetic and parasympathetic nervous systems by measuring tiny changes in adjacent heart beats. Task-related HRV may reflect the association between the flexibility of cognition and ANS function. The study was to investigate the neural mechanism of interactions between ANS and cognitive function in MDD with Magnetoencephalography (MEG) measurements. Participants included 20 MDD patients and 18 healthy controls (HCs). All participants were measured with a go/no-go task MEG. HRV indices, the standard deviation of the average normal-to-normal (NN) interval calculated over short periods (SDANN) and the square root of the mean squared differences of successive NN intervals (RMSSD), were derived from the raw MEG data. Results showed that MDD patients showed decreased SDANN and RMSSD. In MDD patients, both resting-state and task-related RMSSD were related to inhibitory and control dysfunction. In the go/no-go task, many areas in the prefrontal cortex (PFC) are responsible for an individual's inhibitory function. A brain MEG functional connectivity analysis revealed that there were significant differences in four brain regions within the prefrontal cortex (PFC) between MDD patients and HCs. Task-related RMSSD in HCs were related to the functional connectivity between the left middle frontal gyrus and the anterior cingulate cortex (ACC), while in MDD patients, these values were not related to the above functional connectivity but were related to the functional connectivity between the left middle frontal gyrus and insula. However, the resting-state RMSSD value was not related to these significant difference functional connectivity networks in all participants. It concludes that the decreased task-related HRV is associated with inhibitory dysfunction through functional inter-region connectivity in the PFC in MDD, and the task-related HRV can be used as an index of the association between MDD and autonomic dysregulation.

Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs

Many recent literature studies have revealed interesting dynamics patterns of functional brain networks derived from fMRI data. However, it has been rarely explored how functional networks spatially overlap (or interact) and how such connectome-scale network interactions temporally evolve. To explore these unanswered questions, this paper presents a novel framework for spatio-temporal modeling of connectome-scale functional brain network interactions via two main effective computational methodologies. First, to integrate, pool and compare brain networks across individuals and their cognitive states under task performances, we designed a novel group-wise dictionary learning scheme to derive connectome-scale consistent brain network templates that can be used to define the common reference space of brain network interactions. Second, the temporal dynamics of spatial network interactions is modeled by a weighted time-evolving graph, and then a data-driven unsupervised learning algorithm based on the dynamic behavioral mixed-membership model (DBMM) is adopted to identify behavioral patterns of brain networks during the temporal evolution process of spatial overlaps/interactions. Experimental results on the Human Connectome Project (HCP) task fMRI data showed that our methods can reveal meaningful, diverse behavior patterns of connectome-scale network interactions. In particular, those networks' behavior patterns are distinct across HCP tasks such as motor, working memory, language and social tasks, and their dynamics well correspond to the temporal changes of specific task designs. In general, our framework offers a new approach to characterizing human brain function by quantitative description for the temporal evolution of spatial overlaps/interactions of connectome-scale brain networks in a standard reference space.

Elevated cortisol awakening response associated with early life stress and impaired executive function in healthy adult males

Experiencing early life stress (ELS) and subsequent dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis may play a role in the aetiology of mental health disorders. However, the exact mechanisms linking HPA-axis dysregulation with the development of psychopathology have not been fully delineated. Progress in this area is hampered by the complex and often conflicting associations found between markers of HPA-axis function and risk factors for mental health disorders such as impaired executive function (EF) and ELS. This study investigated the association of the cortisol awakening response (CAR) with ELS and EF in a healthy adult male population (n=109, aged 21-63). As previous inconsistencies in CAR and ELS association studies may be the result of not considering ELS-related factors such as cumulative exposure, type of stressor and developmental timing of ELS, these were also investigated. The main findings were that the CAR was significantly elevated in individuals reporting ELS compared to those reporting no ELS (p=0.007) and that an elevated CAR predicted poorer problem solving/planning (p=0.046). Cumulative exposure, type of stressor and developmental timing of ELS were also found to impact significantly on the CAR. These results suggest that ELS is associated with chronic changes in HPA-axis function and that these changes may be associated with impairments in problem solving/planning. Future work should investigate further the neurobiological mechanisms linking ELS, the CAR and EF and their role in conferring risk for the development of mental health disorders.

Neural reuse of action perception circuits for language, concepts and communication

Neurocognitive and neurolinguistics theories make explicit statements relating specialized cognitive and linguistic processes to specific brain loci. These linking hypotheses are in need of neurobiological justification and explanation. Recent mathematical models of human language mechanisms constrained by fundamental neuroscience principles and established knowledge about comparative neuroanatomy offer explanations for where, when and how language is processed in the human brain. In these models, network structure and connectivity along with action- and perception-induced correlation of neuronal activity co-determine neurocognitive mechanisms. Language learning leads to the formation of action perception circuits (APCs) with specific distributions across cortical areas. Cognitive and linguistic processes such as speech production, comprehension, verbal working memory and prediction are modelled by activity dynamics in these APCs, and combinatorial and communicative-interactive knowledge is organized in the dynamics within, and connections between APCs. The network models and, in particular, the concept of distributionally-specific circuits, can account for some previously not well understood facts about the cortical 'hubs' for semantic processing and the motor system's role in language understanding and speech sound recognition. A review of experimental data evaluates predictions of the APC model and alternative theories, also providing detailed discussion of some seemingly contradictory findings. Throughout, recent disputes about the role of mirror neurons and grounded cognition in language and communication are assessed critically.

Critical Motor Involvement in Prediction of Human and Non-biological Motion Trajectories

OBJECTIVES

Adaptive interaction with the environment requires the ability to predict both human and non-biological motion trajectories. Prior accounts of the neurocognitive basis for prediction of these two motion classes may generally be divided into those that posit that non-biological motion trajectories are predicted using the same motor planning and/or simulation mechanisms used for human actions, and those that posit distinct mechanisms for each. Using brain lesion patients and healthy controls, this study examined critical neural substrates and behavioral correlates of human and non-biological motion prediction.

METHODS

Twenty-seven left hemisphere stroke patients and 13 neurologically intact controls performed a visual occlusion task requiring prediction of pantomimed tool use, real tool use, and non-biological motion videos. Patients were also assessed with measures of motor strength and speed, praxis, and action recognition.

RESULTS

Prediction impairment for both human and non-biological motion was associated with limb apraxia and, weakly, with the severity of motor production deficits, but not with action recognition ability. Furthermore, impairment for human and non-biological motion prediction was equivalently associated with lesions in the left inferior parietal cortex, left dorsal frontal cortex, and the left insula.

CONCLUSIONS

These data suggest that motor planning mechanisms associated with specific loci in the sensorimotor network are critical for prediction of spatiotemporal trajectory information characteristic of both human and non-biological motions. (JINS, 2017, 23, 171-184).

Large-scale functional network overlap is a general property of brain functional organization: Reconciling inconsistent fMRI findings from general-linear-model-based analyses

Functional magnetic resonance imaging (fMRI) studies regularly use univariate general-linear-model-based analyses (GLM). Their findings are often inconsistent across different studies, perhaps because of several fundamental brain properties including functional heterogeneity, balanced excitation and inhibition (E/I), and sparseness of neuronal activities. These properties stipulate heterogeneous neuronal activities in the same voxels and likely limit the sensitivity and specificity of GLM. This paper selectively reviews findings of histological and electrophysiological studies and fMRI spatial independent component analysis (sICA) and reports new findings by applying sICA to two existing datasets. The extant and new findings consistently demonstrate several novel features of brain functional organization not revealed by GLM. They include overlap of large-scale functional networks (FNs) and their concurrent opposite modulations, and no significant modulations in activity of most FNs across the whole brain during any task conditions. These novel features of brain functional organization are highly consistent with the brain's properties of functional heterogeneity, balanced E/I, and sparseness of neuronal activity, and may help reconcile inconsistent GLM findings.

Neurophysiology of rule switching in the corticostriatal circuit

The ability to adjust behavioral responses to cues in a changing environment is crucial for survival. Activity in the medial Prefrontal Cortex (mPFC) is thought to both represent rules to guide behavior as well as detect and resolve conflicts between rules in changing contingencies. While lesion and pharmacological studies have supported a crucial role for mPFC in this type of set-shifting, an understanding of how mPFC represents current rules or detects and resolves conflict between different rules is still unclear. Meanwhile, medial dorsal striatum (mDS) receives major projections from mPFC and neural activity of mDS is closely linked to action selection, making the mDS a potential major player for enacting rule-guided action policies. However, exactly what is signaled by mPFC and how this impacts neural signals in mDS is not well known. In this review, we will summarize what is known about neural signals of rules and set shifting in both prefrontal cortex and dorsal striatum, as well as provide questions and directions for future experiments.

Modulation of Frontoparietal Neurovascular Dynamics in Working Memory

Our perception of the world is represented in widespread, overlapping, and interactive neuronal networks of the cerebral cortex. A majority of physiological studies on the subject have focused on oscillatory synchrony as the binding mechanism for representation and transmission of neural information. Little is known, however, about the stability of that synchrony during prolonged cognitive operations that span more than just a few seconds. The present research, in primates, investigated the dynamic patterns of oscillatory synchrony by two complementary recording methods, surface field potentials (SFPs) and near-infrared spectroscopy (NIRS). The signals were first recorded during the resting state to examine intrinsic functional connectivity. The temporal modulation of coactivation was then examined on both signals during performance of working memory (WM) tasks with long delays (memory retention epochs). In both signals, the peristimulus period exhibited characteristic features in frontal and parietal regions. Examination of SFP signals over delays lasting tens of seconds, however, revealed alternations of synchronization and desynchronization. These alternations occurred within the same frequency bands observed in the peristimulus epoch, without a specific correspondence between any definite cognitive process (e.g., WM) and synchrony within a given frequency band. What emerged instead was a correlation between the degree of SFP signal fragmentation (in time, frequency, and brain space) and the complexity and efficiency of the task being performed. In other words, the incidence and extent of SFP transitions between synchronization and desynchronization-rather than the absolute degree of synchrony-augmented in correct task performance compared with incorrect performance or in a control task without WM demand. An opposite relationship was found in NIRS: increasing task complexity induced more uniform, rather than fragmented, NIRS coactivations. These findings indicate that the particular features of neural oscillations cannot be linearly mapped to cognitive functions. Rather, information and the cognitive operations performed on it are primarily reflected in their modulations over time. The increased complexity and fragmentation of electrical frequencies in WM may reflect the activation of hierarchically diverse cognits (cognitive networks) in that condition. Conversely, the homogeneity in coherence of NIRS responses may reflect the cumulative vascular reactions that accompany that neuroelectrical proliferation of frequencies and the longer time constant of the NIRS signal. These findings are directly relevant to the mechanisms mediating cognitive processes and to physiologically based interpretations of functional brain imaging.